Overview

Dataset statistics

Number of variables73
Number of observations18979
Missing cells17966
Missing cells (%)1.3%
Total size in memory10.6 MiB
Average record size in memory584.0 B

Variable types

Numeric64
Text9

Alerts

Loan Date End has 17966 (94.7%) missing valuesMissing
ID has unique valuesUnique
Contract has 649 (3.4%) zerosZeros
Value has 248 (1.3%) zerosZeros
Wage has 237 (1.2%) zerosZeros
Release Clause has 1261 (6.6%) zerosZeros

Reproduction

Analysis started2023-11-04 22:12:15.703324
Analysis finished2023-11-04 22:12:17.241953
Duration1.54 second
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIQUE 

Distinct18979
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226403.3848
Minimum41
Maximum259216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:17.431661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile178602.7
Q1210135
median232418
Q3246922.5
95-th percentile257541.1
Maximum259216
Range259175
Interquartile range (IQR)36787.5

Descriptive statistics

Standard deviation27141.05416
Coefficient of variation (CV)0.119879189
Kurtosis5.833942433
Mean226403.3848
Median Absolute Deviation (MAD)18658
Skewness-1.569371843
Sum4296909840
Variance736636820.8
MonotonicityNot monotonic
2023-11-05T01:12:17.589627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158023 1
 
< 0.1%
191657 1
 
< 0.1%
183596 1
 
< 0.1%
199338 1
 
< 0.1%
254383 1
 
< 0.1%
225705 1
 
< 0.1%
221864 1
 
< 0.1%
232367 1
 
< 0.1%
251303 1
 
< 0.1%
227751 1
 
< 0.1%
Other values (18969) 18969
99.9%
ValueCountFrequency (%)
41 1
< 0.1%
1179 1
< 0.1%
2147 1
< 0.1%
2702 1
< 0.1%
3281 1
< 0.1%
ValueCountFrequency (%)
259216 1
< 0.1%
259214 1
< 0.1%
259212 1
< 0.1%
259211 1
< 0.1%
259209 1
< 0.1%

Name
Text

Distinct17920
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:17.891602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length20
Mean length10.03245693
Min length3

Characters and Unicode

Total characters190406
Distinct characters137
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17160 ?
Unique (%)90.4%

Sample

1st rowL. Messi
2nd rowCristiano Ronaldo
3rd rowJ. Oblak
4th rowK. De Bruyne
5th rowNeymar Jr
ValueCountFrequency (%)
j 1770
 
4.6%
m 1768
 
4.6%
a 1672
 
4.4%
s 1019
 
2.7%
d 965
 
2.5%
l 834
 
2.2%
r 816
 
2.1%
c 725
 
1.9%
t 654
 
1.7%
k 617
 
1.6%
Other values (14121) 27313
71.6%
2023-11-05T01:12:18.415158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19174
 
10.1%
. 15866
 
8.3%
a 14847
 
7.8%
e 12000
 
6.3%
o 10161
 
5.3%
i 9781
 
5.1%
n 9476
 
5.0%
r 9385
 
4.9%
l 6705
 
3.5%
s 5773
 
3.0%
Other values (127) 77238
40.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 116716
61.3%
Uppercase Letter 38388
 
20.2%
Space Separator 19174
 
10.1%
Other Punctuation 15949
 
8.4%
Dash Punctuation 170
 
0.1%
Other Symbol 7
 
< 0.1%
Format 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14847
12.7%
e 12000
10.3%
o 10161
 
8.7%
i 9781
 
8.4%
n 9476
 
8.1%
r 9385
 
8.0%
l 6705
 
5.7%
s 5773
 
4.9%
u 4696
 
4.0%
t 4214
 
3.6%
Other values (71) 29678
25.4%
Uppercase Letter
ValueCountFrequency (%)
M 3783
 
9.9%
A 3163
 
8.2%
S 2913
 
7.6%
J 2622
 
6.8%
B 2240
 
5.8%
C 2205
 
5.7%
D 2073
 
5.4%
R 1928
 
5.0%
L 1884
 
4.9%
G 1677
 
4.4%
Other values (40) 13900
36.2%
Other Punctuation
ValueCountFrequency (%)
. 15866
99.5%
' 83
 
0.5%
Space Separator
ValueCountFrequency (%)
19174
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 170
100.0%
Other Symbol
ValueCountFrequency (%)
7
100.0%
Format
ValueCountFrequency (%)
­ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 155104
81.5%
Common 35302
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14847
 
9.6%
e 12000
 
7.7%
o 10161
 
6.6%
i 9781
 
6.3%
n 9476
 
6.1%
r 9385
 
6.1%
l 6705
 
4.3%
s 5773
 
3.7%
u 4696
 
3.0%
t 4214
 
2.7%
Other values (121) 68066
43.9%
Common
ValueCountFrequency (%)
19174
54.3%
. 15866
44.9%
- 170
 
0.5%
' 83
 
0.2%
7
 
< 0.1%
­ 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186534
98.0%
None 3865
 
2.0%
Specials 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19174
 
10.3%
. 15866
 
8.5%
a 14847
 
8.0%
e 12000
 
6.4%
o 10161
 
5.4%
i 9781
 
5.2%
n 9476
 
5.1%
r 9385
 
5.0%
l 6705
 
3.6%
s 5773
 
3.1%
Other values (46) 73366
39.3%
None
ValueCountFrequency (%)
á 517
13.4%
é 512
13.2%
í 474
12.3%
ć 341
 
8.8%
ó 222
 
5.7%
ñ 150
 
3.9%
ü 149
 
3.9%
ö 130
 
3.4%
ú 108
 
2.8%
ã 86
 
2.2%
Other values (70) 1176
30.4%
Specials
ValueCountFrequency (%)
7
100.0%
Distinct18852
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:18.686874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length35
Median length31
Mean length15.00900996
Min length5

Characters and Unicode

Total characters284856
Distinct characters141
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18731 ?
Unique (%)98.7%

Sample

1st rowLionel Messi
2nd rowC. Ronaldo dos Santos Aveiro
3rd rowJan Oblak
4th rowKevin De Bruyne
5th rowNeymar da Silva Santos Jr.
ValueCountFrequency (%)
de 240
 
0.6%
al 229
 
0.5%
josé 190
 
0.4%
silva 188
 
0.4%
daniel 169
 
0.4%
david 161
 
0.4%
juan 153
 
0.4%
da 145
 
0.3%
carlos 144
 
0.3%
luis 124
 
0.3%
Other values (18437) 41340
96.0%
2023-11-05T01:12:19.169656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 28687
 
10.1%
24104
 
8.5%
e 21752
 
7.6%
i 19095
 
6.7%
o 18907
 
6.6%
n 18296
 
6.4%
r 17560
 
6.2%
l 12434
 
4.4%
s 11188
 
3.9%
u 8361
 
2.9%
Other values (131) 104472
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 217200
76.2%
Uppercase Letter 43108
 
15.1%
Space Separator 24104
 
8.5%
Dash Punctuation 276
 
0.1%
Other Punctuation 166
 
0.1%
Format 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 28687
13.2%
e 21752
10.0%
i 19095
 
8.8%
o 18907
 
8.7%
n 18296
 
8.4%
r 17560
 
8.1%
l 12434
 
5.7%
s 11188
 
5.2%
u 8361
 
3.8%
t 7855
 
3.6%
Other values (72) 53065
24.4%
Uppercase Letter
ValueCountFrequency (%)
M 4333
 
10.1%
A 3604
 
8.4%
S 3422
 
7.9%
J 2963
 
6.9%
C 2540
 
5.9%
B 2429
 
5.6%
D 2266
 
5.3%
R 2250
 
5.2%
L 2097
 
4.9%
G 1913
 
4.4%
Other values (44) 15291
35.5%
Other Punctuation
ValueCountFrequency (%)
' 88
53.0%
. 78
47.0%
Space Separator
ValueCountFrequency (%)
24104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 276
100.0%
Format
ValueCountFrequency (%)
­ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 260308
91.4%
Common 24548
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 28687
 
11.0%
e 21752
 
8.4%
i 19095
 
7.3%
o 18907
 
7.3%
n 18296
 
7.0%
r 17560
 
6.7%
l 12434
 
4.8%
s 11188
 
4.3%
u 8361
 
3.2%
t 7855
 
3.0%
Other values (126) 96173
36.9%
Common
ValueCountFrequency (%)
24104
98.2%
- 276
 
1.1%
' 88
 
0.4%
. 78
 
0.3%
­ 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278428
97.7%
None 6428
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 28687
 
10.3%
24104
 
8.7%
e 21752
 
7.8%
i 19095
 
6.9%
o 18907
 
6.8%
n 18296
 
6.6%
r 17560
 
6.3%
l 12434
 
4.5%
s 11188
 
4.0%
u 8361
 
3.0%
Other values (46) 98044
35.2%
None
ValueCountFrequency (%)
é 1078
16.8%
á 1041
16.2%
í 910
14.2%
ó 368
 
5.7%
ć 342
 
5.3%
ñ 202
 
3.1%
ú 200
 
3.1%
ü 172
 
2.7%
ö 154
 
2.4%
Á 142
 
2.2%
Other values (75) 1819
28.3%
Distinct164
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:19.453910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length21
Mean length7.659571105
Min length4

Characters and Unicode

Total characters145371
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.1%

Sample

1st rowArgentina
2nd rowPortugal
3rd rowSlovenia
4th rowBelgium
5th rowBrazil
ValueCountFrequency (%)
england 1705
 
7.8%
germany 1195
 
5.5%
spain 1065
 
4.9%
france 1003
 
4.6%
argentina 943
 
4.3%
brazil 887
 
4.1%
republic 796
 
3.6%
japan 485
 
2.2%
netherlands 438
 
2.0%
ireland 421
 
1.9%
Other values (182) 12921
59.1%
2023-11-05T01:12:19.927423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 19966
 
13.7%
n 14361
 
9.9%
e 11663
 
8.0%
r 10325
 
7.1%
i 9536
 
6.6%
l 8076
 
5.6%
o 5682
 
3.9%
d 5194
 
3.6%
t 4968
 
3.4%
u 4691
 
3.2%
Other values (43) 50909
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 120544
82.9%
Uppercase Letter 21936
 
15.1%
Space Separator 2880
 
2.0%
Other Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 19966
16.6%
n 14361
11.9%
e 11663
9.7%
r 10325
 
8.6%
i 9536
 
7.9%
l 8076
 
6.7%
o 5682
 
4.7%
d 5194
 
4.3%
t 4968
 
4.1%
u 4691
 
3.9%
Other values (19) 26082
21.6%
Uppercase Letter
ValueCountFrequency (%)
S 3128
14.3%
A 2069
9.4%
E 2028
9.2%
R 1655
 
7.5%
C 1573
 
7.2%
G 1537
 
7.0%
B 1505
 
6.9%
P 1503
 
6.9%
F 1092
 
5.0%
N 1090
 
5.0%
Other values (12) 4756
21.7%
Space Separator
ValueCountFrequency (%)
2880
100.0%
Other Punctuation
ValueCountFrequency (%)
& 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 142480
98.0%
Common 2891
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 19966
14.0%
n 14361
 
10.1%
e 11663
 
8.2%
r 10325
 
7.2%
i 9536
 
6.7%
l 8076
 
5.7%
o 5682
 
4.0%
d 5194
 
3.6%
t 4968
 
3.5%
u 4691
 
3.3%
Other values (41) 48018
33.7%
Common
ValueCountFrequency (%)
2880
99.6%
& 11
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145368
> 99.9%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 19966
 
13.7%
n 14361
 
9.9%
e 11663
 
8.0%
r 10325
 
7.1%
i 9536
 
6.6%
l 8076
 
5.6%
o 5682
 
3.9%
d 5194
 
3.6%
t 4968
 
3.4%
u 4691
 
3.2%
Other values (40) 50906
35.0%
None
ValueCountFrequency (%)
ã 1
33.3%
é 1
33.3%
í 1
33.3%

Age
Real number (ℝ)

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.19410928
Minimum16
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:20.125497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile18
Q121
median25
Q329
95-th percentile33
Maximum53
Range37
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.710520473
Coefficient of variation (CV)0.1869691213
Kurtosis-0.4270829882
Mean25.19410928
Median Absolute Deviation (MAD)4
Skewness0.4044011837
Sum478159
Variance22.18900313
MonotonicityNot monotonic
2023-11-05T01:12:20.267900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
23 1465
 
7.7%
24 1426
 
7.5%
22 1421
 
7.5%
20 1408
 
7.4%
21 1381
 
7.3%
26 1287
 
6.8%
28 1271
 
6.7%
25 1239
 
6.5%
27 1213
 
6.4%
19 1076
 
5.7%
Other values (19) 5792
30.5%
ValueCountFrequency (%)
16 35
 
0.2%
17 289
 
1.5%
18 697
3.7%
19 1076
5.7%
20 1408
7.4%
ValueCountFrequency (%)
53 1
 
< 0.1%
43 2
 
< 0.1%
42 5
 
< 0.1%
41 6
< 0.1%
40 13
0.1%

↓OVA
Real number (ℝ)

Distinct47
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.71863639
Minimum47
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:20.408124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile54
Q161
median66
Q370
95-th percentile77
Maximum93
Range46
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.968998989
Coefficient of variation (CV)0.1060429639
Kurtosis0.01225806293
Mean65.71863639
Median Absolute Deviation (MAD)5
Skewness0.09512241139
Sum1247274
Variance48.5669469
MonotonicityDecreasing
2023-11-05T01:12:22.400419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
65 1179
 
6.2%
66 1157
 
6.1%
64 1152
 
6.1%
67 1112
 
5.9%
63 1086
 
5.7%
68 979
 
5.2%
62 953
 
5.0%
69 941
 
5.0%
70 884
 
4.7%
61 821
 
4.3%
Other values (37) 8715
45.9%
ValueCountFrequency (%)
47 14
 
0.1%
48 41
 
0.2%
49 61
 
0.3%
50 122
0.6%
51 177
0.9%
ValueCountFrequency (%)
93 1
 
< 0.1%
92 1
 
< 0.1%
91 4
< 0.1%
90 6
< 0.1%
89 6
< 0.1%

POT
Real number (ℝ)

Distinct48
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.13641393
Minimum47
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:22.563694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile62
Q167
median71
Q375
95-th percentile82
Maximum95
Range48
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.114634632
Coefficient of variation (CV)0.08595646441
Kurtosis0.07911269491
Mean71.13641393
Median Absolute Deviation (MAD)4
Skewness0.1993007312
Sum1350098
Variance37.38875668
MonotonicityNot monotonic
2023-11-05T01:12:22.708094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
70 1289
 
6.8%
72 1262
 
6.6%
73 1206
 
6.4%
71 1199
 
6.3%
69 1189
 
6.3%
67 1108
 
5.8%
68 1101
 
5.8%
75 1016
 
5.4%
74 1016
 
5.4%
66 945
 
5.0%
Other values (38) 7648
40.3%
ValueCountFrequency (%)
47 1
 
< 0.1%
48 2
 
< 0.1%
49 1
 
< 0.1%
50 2
 
< 0.1%
51 5
< 0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
93 7
< 0.1%
92 5
 
< 0.1%
91 10
0.1%
90 14
0.1%

Club
Text

Distinct682
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:22.958164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length35
Median length25
Mean length13.284525
Min length3

Characters and Unicode

Total characters252127
Distinct characters99
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFC Barcelona
2nd rowJuventus
3rd rowAtlético Madrid
4th rowManchester City
5th rowParis Saint-Germain
ValueCountFrequency (%)
fc 3236
 
7.9%
club 1046
 
2.6%
de 616
 
1.5%
city 563
 
1.4%
united 562
 
1.4%
atlético 397
 
1.0%
al 382
 
0.9%
sc 333
 
0.8%
town 297
 
0.7%
cf 285
 
0.7%
Other values (974) 33151
81.1%
2023-11-05T01:12:23.424834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21889
 
8.7%
a 19962
 
7.9%
e 19200
 
7.6%
n 15097
 
6.0%
o 13755
 
5.5%
i 13519
 
5.4%
r 13327
 
5.3%
l 12131
 
4.8%
t 10889
 
4.3%
C 8729
 
3.5%
Other values (89) 103629
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 177045
70.2%
Uppercase Letter 50267
 
19.9%
Space Separator 21889
 
8.7%
Decimal Number 1484
 
0.6%
Other Punctuation 1049
 
0.4%
Dash Punctuation 339
 
0.1%
Close Punctuation 27
 
< 0.1%
Open Punctuation 27
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 19962
11.3%
e 19200
10.8%
n 15097
 
8.5%
o 13755
 
7.8%
i 13519
 
7.6%
r 13327
 
7.5%
l 12131
 
6.9%
t 10889
 
6.2%
s 8290
 
4.7%
u 6517
 
3.7%
Other values (42) 44358
25.1%
Uppercase Letter
ValueCountFrequency (%)
C 8729
17.4%
F 5708
 
11.4%
S 5173
 
10.3%
A 3779
 
7.5%
B 2529
 
5.0%
K 1979
 
3.9%
M 1977
 
3.9%
L 1804
 
3.6%
R 1772
 
3.5%
P 1736
 
3.5%
Other values (19) 15081
30.0%
Decimal Number
ValueCountFrequency (%)
1 379
25.5%
8 253
17.0%
0 225
15.2%
9 179
12.1%
4 143
 
9.6%
6 109
 
7.3%
3 77
 
5.2%
5 60
 
4.0%
7 30
 
2.0%
2 29
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 912
86.9%
& 60
 
5.7%
' 50
 
4.8%
/ 27
 
2.6%
Space Separator
ValueCountFrequency (%)
21889
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 339
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 227312
90.2%
Common 24815
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 19962
 
8.8%
e 19200
 
8.4%
n 15097
 
6.6%
o 13755
 
6.1%
i 13519
 
5.9%
r 13327
 
5.9%
l 12131
 
5.3%
t 10889
 
4.8%
C 8729
 
3.8%
s 8290
 
3.6%
Other values (71) 92413
40.7%
Common
ValueCountFrequency (%)
21889
88.2%
. 912
 
3.7%
1 379
 
1.5%
- 339
 
1.4%
8 253
 
1.0%
0 225
 
0.9%
9 179
 
0.7%
4 143
 
0.6%
6 109
 
0.4%
3 77
 
0.3%
Other values (8) 310
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 248018
98.4%
None 4109
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21889
 
8.8%
a 19962
 
8.0%
e 19200
 
7.7%
n 15097
 
6.1%
o 13755
 
5.5%
i 13519
 
5.5%
r 13327
 
5.4%
l 12131
 
4.9%
t 10889
 
4.4%
C 8729
 
3.5%
Other values (59) 99520
40.1%
None
ValueCountFrequency (%)
é 751
18.3%
ü 531
12.9%
ó 478
11.6%
ş 307
 
7.5%
ö 248
 
6.0%
í 226
 
5.5%
á 213
 
5.2%
ł 189
 
4.6%
ø 158
 
3.8%
ń 106
 
2.6%
Other values (20) 902
22.0%

Contract
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.63533379
Minimum0
Maximum23
Zeros649
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:23.675343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum23
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.300017471
Coefficient of variation (CV)0.6326839854
Kurtosis4.676631584
Mean3.63533379
Median Absolute Deviation (MAD)1
Skewness1.538374454
Sum68995
Variance5.290080365
MonotonicityNot monotonic
2023-11-05T01:12:23.793027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3 3901
20.6%
2 3665
19.3%
4 3615
19.0%
5 2356
12.4%
1 1990
10.5%
6 1062
 
5.6%
0 649
 
3.4%
7 633
 
3.3%
8 394
 
2.1%
9 244
 
1.3%
Other values (13) 470
 
2.5%
ValueCountFrequency (%)
0 649
 
3.4%
1 1990
10.5%
2 3665
19.3%
3 3901
20.6%
4 3615
19.0%
ValueCountFrequency (%)
23 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
< 0.1%
18 8
< 0.1%
Distinct640
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:23.911899image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length12
Median length11
Mean length4.730965804
Min length2

Characters and Unicode

Total characters89789
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique212 ?
Unique (%)1.1%

Sample

1st rowRW, ST, CF
2nd rowST, LW
3rd rowGK
4th rowCAM, CM
5th rowLW, CAM
ValueCountFrequency (%)
cm 3983
13.1%
cb 3880
12.7%
st 3334
10.9%
cdm 2832
9.3%
lm 2411
7.9%
rm 2383
7.8%
cam 2223
7.3%
gk 2075
6.8%
lb 2062
6.8%
rb 2053
6.7%
Other values (5) 3257
10.7%
2023-11-05T01:12:24.169500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 13832
15.4%
C 13293
14.8%
, 11514
12.8%
11514
12.8%
B 8715
9.7%
L 5911
6.6%
R 5880
6.5%
S 3334
 
3.7%
T 3334
 
3.7%
W 2882
 
3.2%
Other values (5) 9580
10.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 66761
74.4%
Other Punctuation 11514
 
12.8%
Space Separator 11514
 
12.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 13832
20.7%
C 13293
19.9%
B 8715
13.1%
L 5911
8.9%
R 5880
8.8%
S 3334
 
5.0%
T 3334
 
5.0%
W 2882
 
4.3%
D 2832
 
4.2%
A 2223
 
3.3%
Other values (3) 4525
 
6.8%
Other Punctuation
ValueCountFrequency (%)
, 11514
100.0%
Space Separator
ValueCountFrequency (%)
11514
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66761
74.4%
Common 23028
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 13832
20.7%
C 13293
19.9%
B 8715
13.1%
L 5911
8.9%
R 5880
8.8%
S 3334
 
5.0%
T 3334
 
5.0%
W 2882
 
4.3%
D 2832
 
4.2%
A 2223
 
3.3%
Other values (3) 4525
 
6.8%
Common
ValueCountFrequency (%)
, 11514
50.0%
11514
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89789
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 13832
15.4%
C 13293
14.8%
, 11514
12.8%
11514
12.8%
B 8715
9.7%
L 5911
6.6%
R 5880
6.5%
S 3334
 
3.7%
T 3334
 
3.7%
W 2882
 
3.2%
Other values (5) 9580
10.7%

Height
Real number (ℝ)

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.1992202
Minimum155
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:24.314626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum155
5-th percentile170
Q1176
median181
Q3186
95-th percentile192
Maximum206
Range51
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.840033247
Coefficient of variation (CV)0.03774869031
Kurtosis-0.3401081437
Mean181.1992202
Median Absolute Deviation (MAD)5
Skewness-0.03455374637
Sum3438980
Variance46.78605482
MonotonicityNot monotonic
2023-11-05T01:12:24.465847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 1480
 
7.8%
178 1250
 
6.6%
185 1185
 
6.2%
183 1148
 
6.0%
175 1094
 
5.8%
188 931
 
4.9%
182 895
 
4.7%
184 817
 
4.3%
186 816
 
4.3%
177 742
 
3.9%
Other values (40) 8621
45.4%
ValueCountFrequency (%)
155 1
 
< 0.1%
156 1
 
< 0.1%
157 1
 
< 0.1%
158 3
< 0.1%
159 2
< 0.1%
ValueCountFrequency (%)
206 1
 
< 0.1%
203 2
 
< 0.1%
202 4
 
< 0.1%
201 13
0.1%
200 7
< 0.1%

Weight
Real number (ℝ)

Distinct56
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.01849413
Minimum50
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:24.618977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile64
Q170
median75
Q380
95-th percentile87
Maximum110
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.073401753
Coefficient of variation (CV)0.09428877286
Kurtosis0.08815141181
Mean75.01849413
Median Absolute Deviation (MAD)5
Skewness0.2290332636
Sum1423776
Variance50.03301236
MonotonicityNot monotonic
2023-11-05T01:12:24.823029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 1495
 
7.9%
75 1457
 
7.7%
80 1108
 
5.8%
72 1024
 
5.4%
78 996
 
5.2%
74 958
 
5.0%
73 957
 
5.0%
77 893
 
4.7%
76 880
 
4.6%
68 795
 
4.2%
Other values (46) 8416
44.3%
ValueCountFrequency (%)
50 1
 
< 0.1%
52 2
 
< 0.1%
53 1
 
< 0.1%
54 5
 
< 0.1%
55 17
0.1%
ValueCountFrequency (%)
110 1
 
< 0.1%
107 1
 
< 0.1%
104 3
< 0.1%
103 4
< 0.1%
102 4
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:24.941375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.761104379
Min length4

Characters and Unicode

Total characters90361
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeft
2nd rowRight
3rd rowRight
4th rowRight
5th rowRight
ValueCountFrequency (%)
right 14445
76.1%
left 4534
 
23.9%
2023-11-05T01:12:25.197879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 18979
21.0%
R 14445
16.0%
i 14445
16.0%
g 14445
16.0%
h 14445
16.0%
L 4534
 
5.0%
e 4534
 
5.0%
f 4534
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71382
79.0%
Uppercase Letter 18979
 
21.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 18979
26.6%
i 14445
20.2%
g 14445
20.2%
h 14445
20.2%
e 4534
 
6.4%
f 4534
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
R 14445
76.1%
L 4534
 
23.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 90361
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 18979
21.0%
R 14445
16.0%
i 14445
16.0%
g 14445
16.0%
h 14445
16.0%
L 4534
 
5.0%
e 4534
 
5.0%
f 4534
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 18979
21.0%
R 14445
16.0%
i 14445
16.0%
g 14445
16.0%
h 14445
16.0%
L 4534
 
5.0%
e 4534
 
5.0%
f 4534
 
5.0%
Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:25.355483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.225617788
Min length2

Characters and Unicode

Total characters42240
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRW
2nd rowST
3rd rowGK
4th rowCAM
5th rowLW
ValueCountFrequency (%)
cb 3686
19.4%
st 2680
14.1%
cam 2299
12.1%
gk 2075
10.9%
rm 1611
8.5%
cdm 1445
 
7.6%
lb 1086
 
5.7%
rb 1079
 
5.7%
cm 1047
 
5.5%
lm 871
 
4.6%
Other values (5) 1100
 
5.8%
2023-11-05T01:12:25.617909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 8555
20.3%
M 7273
17.2%
B 6389
15.1%
R 3265
 
7.7%
S 2680
 
6.3%
T 2680
 
6.3%
L 2404
 
5.7%
A 2299
 
5.4%
G 2075
 
4.9%
K 2075
 
4.9%
Other values (3) 2545
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 42240
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 8555
20.3%
M 7273
17.2%
B 6389
15.1%
R 3265
 
7.7%
S 2680
 
6.3%
T 2680
 
6.3%
L 2404
 
5.7%
A 2299
 
5.4%
G 2075
 
4.9%
K 2075
 
4.9%
Other values (3) 2545
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42240
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 8555
20.3%
M 7273
17.2%
B 6389
15.1%
R 3265
 
7.7%
S 2680
 
6.3%
T 2680
 
6.3%
L 2404
 
5.7%
A 2299
 
5.4%
G 2075
 
4.9%
K 2075
 
4.9%
Other values (3) 2545
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 8555
20.3%
M 7273
17.2%
B 6389
15.1%
R 3265
 
7.7%
S 2680
 
6.3%
T 2680
 
6.3%
L 2404
 
5.7%
A 2299
 
5.4%
G 2075
 
4.9%
K 2075
 
4.9%
Other values (3) 2545
 
6.0%

Joined
Text

Distinct1869
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:25.779759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.42689288
Min length11

Characters and Unicode

Total characters216871
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique688 ?
Unique (%)3.6%

Sample

1st rowJul 1, 2004
2nd rowJul 10, 2018
3rd rowJul 16, 2014
4th rowAug 30, 2015
5th rowAug 3, 2017
ValueCountFrequency (%)
jul 7046
12.4%
1 6799
 
11.9%
2020 5868
 
10.3%
2019 5623
 
9.9%
jan 4492
 
7.9%
2018 3063
 
5.4%
aug 2800
 
4.9%
2017 1795
 
3.2%
sep 1284
 
2.3%
2016 972
 
1.7%
Other values (53) 17195
30.2%
2023-11-05T01:12:26.099140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37958
17.5%
2 29631
13.7%
0 26238
12.1%
1 25024
11.5%
, 18979
8.8%
J 12339
 
5.7%
u 10647
 
4.9%
l 7046
 
3.2%
9 6688
 
3.1%
n 5293
 
2.4%
Other values (24) 37028
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102997
47.5%
Space Separator 37958
 
17.5%
Lowercase Letter 37958
 
17.5%
Other Punctuation 18979
 
8.8%
Uppercase Letter 18979
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 10647
28.0%
l 7046
18.6%
n 5293
13.9%
a 4977
13.1%
g 2800
 
7.4%
e 2569
 
6.8%
p 1446
 
3.8%
b 955
 
2.5%
c 786
 
2.1%
t 456
 
1.2%
Other values (4) 983
 
2.6%
Decimal Number
ValueCountFrequency (%)
2 29631
28.8%
0 26238
25.5%
1 25024
24.3%
9 6688
 
6.5%
8 4314
 
4.2%
7 2998
 
2.9%
3 2518
 
2.4%
6 2012
 
2.0%
5 1905
 
1.8%
4 1669
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
J 12339
65.0%
A 2962
 
15.6%
S 1284
 
6.8%
F 955
 
5.0%
M 485
 
2.6%
O 456
 
2.4%
D 330
 
1.7%
N 168
 
0.9%
Space Separator
ValueCountFrequency (%)
37958
100.0%
Other Punctuation
ValueCountFrequency (%)
, 18979
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 159934
73.7%
Latin 56937
 
26.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 12339
21.7%
u 10647
18.7%
l 7046
12.4%
n 5293
9.3%
a 4977
8.7%
A 2962
 
5.2%
g 2800
 
4.9%
e 2569
 
4.5%
p 1446
 
2.5%
S 1284
 
2.3%
Other values (12) 5574
9.8%
Common
ValueCountFrequency (%)
37958
23.7%
2 29631
18.5%
0 26238
16.4%
1 25024
15.6%
, 18979
11.9%
9 6688
 
4.2%
8 4314
 
2.7%
7 2998
 
1.9%
3 2518
 
1.6%
6 2012
 
1.3%
Other values (2) 3574
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 216871
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37958
17.5%
2 29631
13.7%
0 26238
12.1%
1 25024
11.5%
, 18979
8.8%
J 12339
 
5.7%
u 10647
 
4.9%
l 7046
 
3.2%
9 6688
 
3.1%
n 5293
 
2.4%
Other values (24) 37028
17.1%

Loan Date End
Text

MISSING 

Distinct24
Distinct (%)2.4%
Missing17966
Missing (%)94.7%
Memory size148.4 KiB
2023-11-05T01:12:26.232343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.97729516
Min length11

Characters and Unicode

Total characters12133
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.9%

Sample

1st rowJun 30, 2021
2nd rowJun 30, 2021
3rd rowJun 30, 2021
4th rowJun 30, 2021
5th rowJun 30, 2021
ValueCountFrequency (%)
2021 882
29.0%
30 796
26.2%
jun 793
26.1%
31 175
 
5.8%
dec 124
 
4.1%
2020 113
 
3.7%
jan 40
 
1.3%
may 32
 
1.1%
nov 18
 
0.6%
2022 17
 
0.6%
Other values (11) 49
 
1.6%
2023-11-05T01:12:26.482030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2066
17.0%
2026
16.7%
0 1922
15.8%
1 1075
8.9%
, 1013
8.3%
3 982
8.1%
J 836
6.9%
n 833
6.9%
u 799
 
6.6%
D 124
 
1.0%
Other values (14) 457
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6055
49.9%
Space Separator 2026
 
16.7%
Lowercase Letter 2026
 
16.7%
Other Punctuation 1013
 
8.3%
Uppercase Letter 1013
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 833
41.1%
u 799
39.4%
e 124
 
6.1%
c 124
 
6.1%
a 72
 
3.6%
y 32
 
1.6%
o 18
 
0.9%
v 18
 
0.9%
g 3
 
0.1%
l 3
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 2066
34.1%
0 1922
31.7%
1 1075
17.8%
3 982
16.2%
7 7
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
J 836
82.5%
D 124
 
12.2%
M 32
 
3.2%
N 18
 
1.8%
A 3
 
0.3%
Space Separator
ValueCountFrequency (%)
2026
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1013
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9094
75.0%
Latin 3039
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 836
27.5%
n 833
27.4%
u 799
26.3%
D 124
 
4.1%
e 124
 
4.1%
c 124
 
4.1%
a 72
 
2.4%
y 32
 
1.1%
M 32
 
1.1%
N 18
 
0.6%
Other values (5) 45
 
1.5%
Common
ValueCountFrequency (%)
2 2066
22.7%
2026
22.3%
0 1922
21.1%
1 1075
11.8%
, 1013
11.1%
3 982
10.8%
7 7
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12133
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2066
17.0%
2026
16.7%
0 1922
15.8%
1 1075
8.9%
, 1013
8.3%
3 982
8.1%
J 836
6.9%
n 833
6.9%
u 799
 
6.6%
D 124
 
1.0%
Other values (14) 457
 
3.8%

Value
Real number (ℝ)

ZEROS 

Distinct255
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2865062.909
Minimum0
Maximum185500000
Zeros248
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:26.640066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile150000
Q1475000
median950000
Q32000000
95-th percentile11500000
Maximum185500000
Range185500000
Interquartile range (IQR)1525000

Descriptive statistics

Standard deviation7685154.452
Coefficient of variation (CV)2.682368484
Kurtosis91.99606675
Mean2865062.909
Median Absolute Deviation (MAD)625000
Skewness7.998518165
Sum5.437602896 × 1010
Variance5.906159896 × 1013
MonotonicityNot monotonic
2023-11-05T01:12:26.896024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200000 582
 
3.1%
1100000 559
 
2.9%
1300000 534
 
2.8%
1600000 532
 
2.8%
1000000 497
 
2.6%
1400000 467
 
2.5%
1500000 392
 
2.1%
325000 392
 
2.1%
475000 386
 
2.0%
500000 383
 
2.0%
Other values (245) 14255
75.1%
ValueCountFrequency (%)
0 248
1.3%
9000 1
 
< 0.1%
15000 2
 
< 0.1%
20000 1
 
< 0.1%
25000 14
 
0.1%
ValueCountFrequency (%)
185500000 1
< 0.1%
132000000 1
< 0.1%
129000000 1
< 0.1%
124000000 1
< 0.1%
121000000 1
< 0.1%

Wage
Real number (ℝ)

ZEROS 

Distinct134
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9092.062279
Minimum0
Maximum560000
Zeros237
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:27.143073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile500
Q11000
median3000
Q38000
95-th percentile38000
Maximum560000
Range560000
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation19707.02109
Coefficient of variation (CV)2.167497371
Kurtosis93.46081046
Mean9092.062279
Median Absolute Deviation (MAD)2250
Skewness7.30184754
Sum172558250
Variance388366680.2
MonotonicityNot monotonic
2023-11-05T01:12:27.355258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 2899
15.3%
500 2042
 
10.8%
1000 1891
 
10.0%
3000 1823
 
9.6%
4000 1050
 
5.5%
5000 956
 
5.0%
6000 742
 
3.9%
7000 507
 
2.7%
8000 452
 
2.4%
9000 407
 
2.1%
Other values (124) 6210
32.7%
ValueCountFrequency (%)
0 237
 
1.2%
500 2042
10.8%
550 183
 
1.0%
600 209
 
1.1%
650 174
 
0.9%
ValueCountFrequency (%)
560000 1
< 0.1%
370000 1
< 0.1%
350000 2
< 0.1%
310000 2
< 0.1%
300000 2
< 0.1%

Release Clause
Real number (ℝ)

ZEROS 

Distinct1216
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3962951.309
Minimum0
Maximum203100000
Zeros1261
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:27.580868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1423500
median1000000
Q32800000
95-th percentile17900000
Maximum203100000
Range203100000
Interquartile range (IQR)2376500

Descriptive statistics

Standard deviation9772761.891
Coefficient of variation (CV)2.46603128
Kurtosis70.647027
Mean3962951.309
Median Absolute Deviation (MAD)737000
Skewness6.886444408
Sum7.52128529 × 1010
Variance9.550687497 × 1013
MonotonicityNot monotonic
2023-11-05T01:12:27.816756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1261
 
6.6%
1100000 609
 
3.2%
1200000 504
 
2.7%
1300000 443
 
2.3%
1400000 385
 
2.0%
1500000 340
 
1.8%
1000000 328
 
1.7%
1600000 318
 
1.7%
1700000 285
 
1.5%
1800000 263
 
1.4%
Other values (1206) 14243
75.0%
ValueCountFrequency (%)
0 1261
6.6%
9000 1
 
< 0.1%
12000 1
 
< 0.1%
13000 1
 
< 0.1%
22000 1
 
< 0.1%
ValueCountFrequency (%)
203100000 1
< 0.1%
166500000 1
< 0.1%
161000000 1
< 0.1%
159400000 1
< 0.1%
147700000 1
< 0.1%

Attacking
Real number (ℝ)

Distinct367
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.9381422
Minimum42
Maximum437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:28.075865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile74
Q1222
median263
Q3297
95-th percentile343
Maximum437
Range395
Interquartile range (IQR)75

Descriptive statistics

Standard deviation74.29942845
Coefficient of variation (CV)0.2984654252
Kurtosis0.794554593
Mean248.9381422
Median Absolute Deviation (MAD)37
Skewness-1.059844338
Sum4724597
Variance5520.405068
MonotonicityNot monotonic
2023-11-05T01:12:28.284958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
277 169
 
0.9%
261 167
 
0.9%
256 162
 
0.9%
279 160
 
0.8%
266 155
 
0.8%
281 147
 
0.8%
280 145
 
0.8%
254 145
 
0.8%
284 145
 
0.8%
292 144
 
0.8%
Other values (357) 17440
91.9%
ValueCountFrequency (%)
42 2
< 0.1%
43 2
< 0.1%
45 4
< 0.1%
46 1
 
< 0.1%
47 2
< 0.1%
ValueCountFrequency (%)
437 1
< 0.1%
429 2
< 0.1%
426 1
< 0.1%
425 1
< 0.1%
423 1
< 0.1%

Crossing
Real number (ℝ)

Distinct89
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.68839243
Minimum6
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:28.491083image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile13
Q138
median54
Q363
95-th percentile74
Maximum94
Range88
Interquartile range (IQR)25

Descriptive statistics

Standard deviation18.1311529
Coefficient of variation (CV)0.3648971523
Kurtosis-0.4625183743
Mean49.68839243
Median Absolute Deviation (MAD)12
Skewness-0.6176443193
Sum943036
Variance328.7387055
MonotonicityNot monotonic
2023-11-05T01:12:28.737195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 559
 
2.9%
59 554
 
2.9%
65 553
 
2.9%
62 543
 
2.9%
60 539
 
2.8%
64 518
 
2.7%
63 492
 
2.6%
61 460
 
2.4%
68 456
 
2.4%
55 451
 
2.4%
Other values (79) 13854
73.0%
ValueCountFrequency (%)
6 3
 
< 0.1%
7 9
 
< 0.1%
8 31
 
0.2%
9 43
 
0.2%
10 177
0.9%
ValueCountFrequency (%)
94 1
 
< 0.1%
93 1
 
< 0.1%
92 1
 
< 0.1%
91 1
 
< 0.1%
90 3
< 0.1%

Finishing
Real number (ℝ)

Distinct93
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.84240476
Minimum3
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:29.361087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q130
median49
Q362
95-th percentile73
Maximum95
Range92
Interquartile range (IQR)32

Descriptive statistics

Standard deviation19.56708136
Coefficient of variation (CV)0.426833659
Kurtosis-0.8967076511
Mean45.84240476
Median Absolute Deviation (MAD)15
Skewness-0.3436364408
Sum870043
Variance382.870673
MonotonicityNot monotonic
2023-11-05T01:12:29.503139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 501
 
2.6%
60 479
 
2.5%
62 478
 
2.5%
55 436
 
2.3%
64 431
 
2.3%
65 431
 
2.3%
63 420
 
2.2%
59 401
 
2.1%
61 399
 
2.1%
66 396
 
2.1%
Other values (83) 14607
77.0%
ValueCountFrequency (%)
3 5
 
< 0.1%
4 8
 
< 0.1%
5 120
0.6%
6 128
0.7%
7 184
1.0%
ValueCountFrequency (%)
95 2
< 0.1%
94 3
< 0.1%
93 1
 
< 0.1%
92 2
< 0.1%
91 3
< 0.1%

Heading Accuracy
Real number (ℝ)

Distinct89
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.94272617
Minimum5
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:29.643149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q144
median55
Q364
95-th percentile75
Maximum93
Range88
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.29440869
Coefficient of variation (CV)0.3329515019
Kurtosis0.2937327919
Mean51.94272617
Median Absolute Deviation (MAD)10
Skewness-0.86802397
Sum985821
Variance299.096572
MonotonicityNot monotonic
2023-11-05T01:12:29.784185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 704
 
3.7%
62 583
 
3.1%
55 579
 
3.1%
60 577
 
3.0%
59 537
 
2.8%
65 528
 
2.8%
64 513
 
2.7%
52 505
 
2.7%
54 504
 
2.7%
56 497
 
2.6%
Other values (79) 13452
70.9%
ValueCountFrequency (%)
5 3
 
< 0.1%
6 1
 
< 0.1%
7 8
 
< 0.1%
8 22
0.1%
9 30
0.2%
ValueCountFrequency (%)
93 2
 
< 0.1%
92 3
< 0.1%
91 3
< 0.1%
90 7
< 0.1%
89 1
 
< 0.1%

Short Passing
Real number (ℝ)

Distinct86
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.76811212
Minimum7
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:29.929094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile25
Q154
median62
Q368
95-th percentile77
Maximum94
Range87
Interquartile range (IQR)14

Descriptive statistics

Standard deviation14.51910567
Coefficient of variation (CV)0.2470575478
Kurtosis1.01492759
Mean58.76811212
Median Absolute Deviation (MAD)7
Skewness-1.157418451
Sum1115360
Variance210.8044294
MonotonicityNot monotonic
2023-11-05T01:12:30.080154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 900
 
4.7%
64 862
 
4.5%
65 847
 
4.5%
66 815
 
4.3%
68 764
 
4.0%
67 734
 
3.9%
63 731
 
3.9%
60 720
 
3.8%
61 647
 
3.4%
58 643
 
3.4%
Other values (76) 11316
59.6%
ValueCountFrequency (%)
7 1
 
< 0.1%
8 4
 
< 0.1%
11 8
< 0.1%
12 13
0.1%
13 13
0.1%
ValueCountFrequency (%)
94 1
 
< 0.1%
93 1
 
< 0.1%
92 2
 
< 0.1%
91 2
 
< 0.1%
90 8
< 0.1%

Volleys
Real number (ℝ)

Distinct88
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.69650667
Minimum3
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:30.214245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q130
median44
Q356
95-th percentile70
Maximum90
Range87
Interquartile range (IQR)26

Descriptive statistics

Standard deviation17.64693651
Coefficient of variation (CV)0.413311015
Kurtosis-0.6546941748
Mean42.69650667
Median Absolute Deviation (MAD)13
Skewness-0.1682069749
Sum810337
Variance311.4143681
MonotonicityNot monotonic
2023-11-05T01:12:30.354985image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 448
 
2.4%
49 431
 
2.3%
55 411
 
2.2%
42 400
 
2.1%
59 400
 
2.1%
45 393
 
2.1%
56 391
 
2.1%
53 384
 
2.0%
52 374
 
2.0%
32 369
 
1.9%
Other values (78) 14978
78.9%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 16
 
0.1%
5 123
0.6%
6 135
0.7%
7 155
0.8%
ValueCountFrequency (%)
90 2
 
< 0.1%
89 2
 
< 0.1%
88 4
< 0.1%
87 3
< 0.1%
86 6
< 0.1%

Skill
Real number (ℝ)

Distinct393
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.4792139
Minimum40
Maximum470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:30.491903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile83
Q1222
median269
Q3310
95-th percentile363
Maximum470
Range430
Interquartile range (IQR)88

Descriptive statistics

Standard deviation78.65060134
Coefficient of variation (CV)0.3066548753
Kurtosis0.2240918171
Mean256.4792139
Median Absolute Deviation (MAD)43
Skewness-0.8010070901
Sum4867719
Variance6185.917091
MonotonicityNot monotonic
2023-11-05T01:12:30.626402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
264 144
 
0.8%
285 142
 
0.7%
289 139
 
0.7%
301 137
 
0.7%
309 133
 
0.7%
267 131
 
0.7%
284 131
 
0.7%
272 131
 
0.7%
260 131
 
0.7%
292 130
 
0.7%
Other values (383) 17630
92.9%
ValueCountFrequency (%)
40 1
< 0.1%
43 2
< 0.1%
46 1
< 0.1%
47 2
< 0.1%
48 1
< 0.1%
ValueCountFrequency (%)
470 1
< 0.1%
448 1
< 0.1%
441 1
< 0.1%
440 1
< 0.1%
439 1
< 0.1%

Dribbling
Real number (ℝ)

Distinct91
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.58749144
Minimum5
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:30.762228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q149
median61
Q368
95-th percentile77
Maximum96
Range91
Interquartile range (IQR)19

Descriptive statistics

Standard deviation18.76131404
Coefficient of variation (CV)0.3375096366
Kurtosis0.5159837561
Mean55.58749144
Median Absolute Deviation (MAD)8
Skewness-1.135203474
Sum1054995
Variance351.9869043
MonotonicityNot monotonic
2023-11-05T01:12:30.911893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 778
 
4.1%
64 749
 
3.9%
63 705
 
3.7%
62 689
 
3.6%
66 648
 
3.4%
68 638
 
3.4%
67 623
 
3.3%
61 609
 
3.2%
60 584
 
3.1%
70 555
 
2.9%
Other values (81) 12401
65.3%
ValueCountFrequency (%)
5 40
 
0.2%
6 57
0.3%
7 73
0.4%
8 102
0.5%
9 102
0.5%
ValueCountFrequency (%)
96 1
 
< 0.1%
95 1
 
< 0.1%
93 1
 
< 0.1%
92 6
< 0.1%
91 3
< 0.1%

Curve
Real number (ℝ)

Distinct91
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.24695716
Minimum4
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:31.052028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile13
Q135
median49
Q361
95-th percentile75
Maximum94
Range90
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.20779045
Coefficient of variation (CV)0.3853748802
Kurtosis-0.6639992827
Mean47.24695716
Median Absolute Deviation (MAD)13
Skewness-0.2612581599
Sum896700
Variance331.5236332
MonotonicityNot monotonic
2023-11-05T01:12:31.191468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 438
 
2.3%
59 420
 
2.2%
55 419
 
2.2%
58 413
 
2.2%
45 402
 
2.1%
52 391
 
2.1%
60 389
 
2.0%
42 379
 
2.0%
49 375
 
2.0%
57 375
 
2.0%
Other values (81) 14978
78.9%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 7
 
< 0.1%
8 18
0.1%
ValueCountFrequency (%)
94 1
 
< 0.1%
93 1
 
< 0.1%
92 1
 
< 0.1%
91 1
 
< 0.1%
90 4
< 0.1%

FK Accuracy
Real number (ℝ)

Distinct90
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.39101112
Minimum5
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:31.328038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q131
median41
Q355
95-th percentile71
Maximum94
Range89
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.22794693
Coefficient of variation (CV)0.4064056619
Kurtosis-0.6450836362
Mean42.39101112
Median Absolute Deviation (MAD)12
Skewness0.1176378307
Sum804539
Variance296.8021555
MonotonicityNot monotonic
2023-11-05T01:12:31.468590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 522
 
2.8%
39 499
 
2.6%
42 489
 
2.6%
32 487
 
2.6%
40 475
 
2.5%
31 463
 
2.4%
38 458
 
2.4%
34 445
 
2.3%
30 429
 
2.3%
37 422
 
2.2%
Other values (80) 14290
75.3%
ValueCountFrequency (%)
5 2
 
< 0.1%
6 7
 
< 0.1%
7 11
 
0.1%
8 23
0.1%
9 31
0.2%
ValueCountFrequency (%)
94 1
 
< 0.1%
93 1
 
< 0.1%
92 1
 
< 0.1%
91 3
< 0.1%
90 1
 
< 0.1%

Long Passing
Real number (ℝ)

Distinct86
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.72538068
Minimum5
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:31.605416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile23
Q143
median56
Q364
95-th percentile73
Maximum93
Range88
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.17815078
Coefficient of variation (CV)0.2878718102
Kurtosis-0.3284620955
Mean52.72538068
Median Absolute Deviation (MAD)9
Skewness-0.59097651
Sum1000675
Variance230.3762612
MonotonicityNot monotonic
2023-11-05T01:12:31.745978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 650
 
3.4%
60 645
 
3.4%
58 618
 
3.3%
59 613
 
3.2%
65 605
 
3.2%
63 577
 
3.0%
64 576
 
3.0%
55 556
 
2.9%
61 539
 
2.8%
57 529
 
2.8%
Other values (76) 13071
68.9%
ValueCountFrequency (%)
5 1
 
< 0.1%
8 1
 
< 0.1%
9 5
< 0.1%
10 1
 
< 0.1%
11 12
0.1%
ValueCountFrequency (%)
93 2
 
< 0.1%
91 2
 
< 0.1%
90 1
 
< 0.1%
89 6
< 0.1%
88 2
 
< 0.1%

Ball Control
Real number (ℝ)

Distinct91
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.52837347
Minimum5
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:31.888221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile18
Q154
median63
Q369
95-th percentile77
Maximum96
Range91
Interquartile range (IQR)15

Descriptive statistics

Standard deviation16.5658922
Coefficient of variation (CV)0.2830403652
Kurtosis1.278962052
Mean58.52837347
Median Absolute Deviation (MAD)7
Skewness-1.341846967
Sum1110810
Variance274.4287844
MonotonicityNot monotonic
2023-11-05T01:12:32.114133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 890
 
4.7%
62 849
 
4.5%
64 847
 
4.5%
68 749
 
3.9%
66 748
 
3.9%
63 747
 
3.9%
60 721
 
3.8%
70 712
 
3.8%
67 712
 
3.8%
61 667
 
3.5%
Other values (81) 11337
59.7%
ValueCountFrequency (%)
5 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 6
 
< 0.1%
10 53
0.3%
ValueCountFrequency (%)
96 1
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
93 1
 
< 0.1%
92 3
< 0.1%

Movement
Real number (ℝ)

Distinct331
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean317.7186891
Minimum122
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:32.278169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile204
Q1289
median327
Q3356
95-th percentile394
Maximum464
Range342
Interquartile range (IQR)67

Descriptive statistics

Standard deviation55.87904577
Coefficient of variation (CV)0.175875854
Kurtosis0.5871265072
Mean317.7186891
Median Absolute Deviation (MAD)32
Skewness-0.7945759565
Sum6029983
Variance3122.467756
MonotonicityNot monotonic
2023-11-05T01:12:32.445983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
340 208
 
1.1%
326 183
 
1.0%
330 181
 
1.0%
329 176
 
0.9%
338 173
 
0.9%
355 171
 
0.9%
350 170
 
0.9%
354 168
 
0.9%
343 168
 
0.9%
339 168
 
0.9%
Other values (321) 17213
90.7%
ValueCountFrequency (%)
122 1
< 0.1%
124 1
< 0.1%
125 2
< 0.1%
126 2
< 0.1%
127 1
< 0.1%
ValueCountFrequency (%)
464 1
< 0.1%
460 2
< 0.1%
458 1
< 0.1%
453 2
< 0.1%
451 1
< 0.1%

Acceleration
Real number (ℝ)

Distinct85
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.35755308
Minimum13
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:32.605981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile33
Q157
median67
Q374
95-th percentile85
Maximum97
Range84
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.88998126
Coefficient of variation (CV)0.2313633839
Kurtosis0.6337077222
Mean64.35755308
Median Absolute Deviation (MAD)8
Skewness-0.8626585747
Sum1221442
Variance221.711542
MonotonicityNot monotonic
2023-11-05T01:12:32.869636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 779
 
4.1%
69 715
 
3.8%
67 690
 
3.6%
66 649
 
3.4%
74 642
 
3.4%
65 623
 
3.3%
72 606
 
3.2%
73 591
 
3.1%
64 578
 
3.0%
75 565
 
3.0%
Other values (75) 12541
66.1%
ValueCountFrequency (%)
13 2
 
< 0.1%
14 2
 
< 0.1%
15 22
0.1%
16 13
0.1%
17 30
0.2%
ValueCountFrequency (%)
97 1
 
< 0.1%
96 6
 
< 0.1%
95 7
 
< 0.1%
94 27
0.1%
93 45
0.2%

Sprint Speed
Real number (ℝ)

Distinct84
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.41293008
Minimum12
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:33.029303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile34
Q157
median67
Q374
95-th percentile84
Maximum96
Range84
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.63874331
Coefficient of variation (CV)0.2272640492
Kurtosis0.7076970716
Mean64.41293008
Median Absolute Deviation (MAD)8
Skewness-0.880458864
Sum1222493
Variance214.2928057
MonotonicityNot monotonic
2023-11-05T01:12:33.224949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 787
 
4.1%
68 785
 
4.1%
67 738
 
3.9%
66 683
 
3.6%
65 664
 
3.5%
64 628
 
3.3%
72 617
 
3.3%
74 612
 
3.2%
73 585
 
3.1%
70 582
 
3.1%
Other values (74) 12298
64.8%
ValueCountFrequency (%)
12 1
 
< 0.1%
14 1
 
< 0.1%
15 27
0.1%
16 17
0.1%
17 27
0.1%
ValueCountFrequency (%)
96 4
 
< 0.1%
95 7
 
< 0.1%
94 24
0.1%
93 40
0.2%
92 56
0.3%

Agility
Real number (ℝ)

Distinct81
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.36672111
Minimum14
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:33.392025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile34
Q155
median66
Q374
95-th percentile84
Maximum96
Range82
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.59627701
Coefficient of variation (CV)0.230346099
Kurtosis0.01551631844
Mean63.36672111
Median Absolute Deviation (MAD)9
Skewness-0.6290347997
Sum1202637
Variance213.0513026
MonotonicityNot monotonic
2023-11-05T01:12:33.539814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 632
 
3.3%
67 616
 
3.2%
70 615
 
3.2%
72 602
 
3.2%
66 593
 
3.1%
69 590
 
3.1%
65 578
 
3.0%
73 577
 
3.0%
71 561
 
3.0%
74 552
 
2.9%
Other values (71) 13063
68.8%
ValueCountFrequency (%)
14 1
 
< 0.1%
15 2
< 0.1%
18 2
< 0.1%
19 3
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
96 1
 
< 0.1%
95 5
 
< 0.1%
94 15
 
0.1%
93 32
0.2%
92 50
0.3%

Reactions
Real number (ℝ)

Distinct69
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.65561937
Minimum24
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:33.684120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile47
Q156
median62
Q368
95-th percentile76
Maximum95
Range71
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.07211401
Coefficient of variation (CV)0.1471417221
Kurtosis0.1926182263
Mean61.65561937
Median Absolute Deviation (MAD)6
Skewness-0.1121582844
Sum1170162
Variance82.30325262
MonotonicityNot monotonic
2023-11-05T01:12:33.891819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 924
 
4.9%
58 915
 
4.8%
63 854
 
4.5%
62 842
 
4.4%
65 840
 
4.4%
64 832
 
4.4%
59 786
 
4.1%
66 765
 
4.0%
68 737
 
3.9%
61 714
 
3.8%
Other values (59) 10770
56.7%
ValueCountFrequency (%)
24 1
 
< 0.1%
28 1
 
< 0.1%
29 1
 
< 0.1%
30 6
< 0.1%
31 12
0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 2
 
< 0.1%
93 2
 
< 0.1%
92 6
< 0.1%
91 4
< 0.1%

Balance
Real number (ℝ)

Distinct82
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.92586543
Minimum12
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:34.055268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile36
Q156
median66
Q374
95-th percentile84
Maximum97
Range85
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.06228466
Coefficient of variation (CV)0.2199780099
Kurtosis0.1101749523
Mean63.92586543
Median Absolute Deviation (MAD)9
Skewness-0.6076080285
Sum1213249
Variance197.7478498
MonotonicityNot monotonic
2023-11-05T01:12:34.209590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 673
 
3.5%
65 638
 
3.4%
70 625
 
3.3%
67 622
 
3.3%
69 608
 
3.2%
72 605
 
3.2%
71 604
 
3.2%
66 593
 
3.1%
75 588
 
3.1%
73 579
 
3.1%
Other values (72) 12844
67.7%
ValueCountFrequency (%)
12 1
 
< 0.1%
17 2
 
< 0.1%
18 1
 
< 0.1%
19 2
 
< 0.1%
20 7
< 0.1%
ValueCountFrequency (%)
97 1
 
< 0.1%
96 1
 
< 0.1%
95 9
 
< 0.1%
94 24
0.1%
93 31
0.2%

Power
Real number (ℝ)

Distinct288
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296.6051952
Minimum122
Maximum444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:34.348776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile201
Q1264
median302
Q3334
95-th percentile371
Maximum444
Range322
Interquartile range (IQR)70

Descriptive statistics

Standard deviation50.73238963
Coefficient of variation (CV)0.1710434964
Kurtosis-0.1753757332
Mean296.6051952
Median Absolute Deviation (MAD)34
Skewness-0.4560131499
Sum5629270
Variance2573.775357
MonotonicityNot monotonic
2023-11-05T01:12:34.498034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
322 172
 
0.9%
326 165
 
0.9%
336 162
 
0.9%
314 159
 
0.8%
325 158
 
0.8%
331 157
 
0.8%
301 157
 
0.8%
304 157
 
0.8%
305 156
 
0.8%
310 154
 
0.8%
Other values (278) 17382
91.6%
ValueCountFrequency (%)
122 1
< 0.1%
128 1
< 0.1%
134 1
< 0.1%
139 2
< 0.1%
140 1
< 0.1%
ValueCountFrequency (%)
444 1
< 0.1%
437 1
< 0.1%
430 1
< 0.1%
429 1
< 0.1%
427 2
< 0.1%

Shot Power
Real number (ℝ)

Distinct76
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.80167554
Minimum18
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:35.162236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile35
Q148
median59
Q368
95-th percentile78
Maximum95
Range77
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.30874704
Coefficient of variation (CV)0.2302484645
Kurtosis-0.53558176
Mean57.80167554
Median Absolute Deviation (MAD)10
Skewness-0.2322170757
Sum1097018
Variance177.1227477
MonotonicityNot monotonic
2023-11-05T01:12:35.327172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 618
 
3.3%
70 568
 
3.0%
68 564
 
3.0%
64 561
 
3.0%
62 549
 
2.9%
58 529
 
2.8%
66 522
 
2.8%
59 511
 
2.7%
60 494
 
2.6%
55 493
 
2.6%
Other values (66) 13570
71.5%
ValueCountFrequency (%)
18 2
 
< 0.1%
20 13
0.1%
21 13
0.1%
22 21
0.1%
23 31
0.2%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 2
 
< 0.1%
93 1
 
< 0.1%
91 2
 
< 0.1%
90 8
< 0.1%

Jumping
Real number (ℝ)

Distinct75
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.57737499
Minimum15
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:35.490234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile43
Q158
median65
Q373
95-th percentile83
Maximum95
Range80
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.84486952
Coefficient of variation (CV)0.1834213534
Kurtosis0.2240816356
Mean64.57737499
Median Absolute Deviation (MAD)7
Skewness-0.4055605481
Sum1225614
Variance140.300934
MonotonicityNot monotonic
2023-11-05T01:12:35.657578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 803
 
4.2%
71 714
 
3.8%
65 671
 
3.5%
62 661
 
3.5%
72 651
 
3.4%
64 650
 
3.4%
60 638
 
3.4%
68 632
 
3.3%
66 629
 
3.3%
67 628
 
3.3%
Other values (65) 12302
64.8%
ValueCountFrequency (%)
15 1
< 0.1%
17 1
< 0.1%
19 1
< 0.1%
22 1
< 0.1%
24 1
< 0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 6
 
< 0.1%
93 22
 
0.1%
92 43
0.2%
91 72
0.4%

Stamina
Real number (ℝ)

Distinct85
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.65398598
Minimum12
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:35.838320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile28
Q155
median66
Q373
95-th percentile84
Maximum97
Range85
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.80422286
Coefficient of variation (CV)0.2522460879
Kurtosis0.4947909738
Mean62.65398598
Median Absolute Deviation (MAD)8
Skewness-0.9006430667
Sum1189110
Variance249.7734601
MonotonicityNot monotonic
2023-11-05T01:12:36.033330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 754
 
4.0%
70 656
 
3.5%
69 646
 
3.4%
67 642
 
3.4%
65 627
 
3.3%
66 625
 
3.3%
71 617
 
3.3%
72 613
 
3.2%
64 588
 
3.1%
74 568
 
3.0%
Other values (75) 12643
66.6%
ValueCountFrequency (%)
12 1
 
< 0.1%
14 3
 
< 0.1%
15 5
 
< 0.1%
16 17
0.1%
17 41
0.2%
ValueCountFrequency (%)
97 1
 
< 0.1%
96 1
 
< 0.1%
95 3
 
< 0.1%
94 13
0.1%
93 26
0.1%

Strength
Real number (ℝ)

Distinct77
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.78692239
Minimum16
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:36.226854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile41
Q157
median66
Q374
95-th percentile84
Maximum97
Range81
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.48867189
Coefficient of variation (CV)0.192765321
Kurtosis0.008950610998
Mean64.78692239
Median Absolute Deviation (MAD)8
Skewness-0.4187231044
Sum1229591
Variance155.9669256
MonotonicityNot monotonic
2023-11-05T01:12:36.433328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 704
 
3.7%
70 684
 
3.6%
67 630
 
3.3%
66 625
 
3.3%
72 609
 
3.2%
65 595
 
3.1%
71 594
 
3.1%
64 593
 
3.1%
73 576
 
3.0%
69 573
 
3.0%
Other values (67) 12796
67.4%
ValueCountFrequency (%)
16 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
24 2
< 0.1%
25 2
< 0.1%
ValueCountFrequency (%)
97 1
 
< 0.1%
96 1
 
< 0.1%
95 4
 
< 0.1%
94 10
 
0.1%
93 34
0.2%

Long Shots
Real number (ℝ)

Distinct91
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.78523631
Minimum4
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:36.616759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q132
median51
Q362
95-th percentile73
Maximum94
Range90
Interquartile range (IQR)30

Descriptive statistics

Standard deviation19.30053429
Coefficient of variation (CV)0.4125347185
Kurtosis-0.783129079
Mean46.78523631
Median Absolute Deviation (MAD)14
Skewness-0.4422130067
Sum887937
Variance372.510624
MonotonicityNot monotonic
2023-11-05T01:12:36.797794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 497
 
2.6%
58 490
 
2.6%
59 479
 
2.5%
62 458
 
2.4%
65 456
 
2.4%
55 447
 
2.4%
63 446
 
2.3%
52 430
 
2.3%
56 419
 
2.2%
57 410
 
2.2%
Other values (81) 14447
76.1%
ValueCountFrequency (%)
4 9
 
< 0.1%
5 117
0.6%
6 157
0.8%
7 149
0.8%
8 176
0.9%
ValueCountFrequency (%)
94 1
< 0.1%
93 1
< 0.1%
92 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%

Mentality
Real number (ℝ)

Distinct351
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.1997471
Minimum50
Maximum421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:36.954000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile105
Q1227.5
median263
Q3297
95-th percentile341.1
Maximum421
Range371
Interquartile range (IQR)69.5

Descriptive statistics

Standard deviation64.59561286
Coefficient of variation (CV)0.2541136
Kurtosis0.7870619552
Mean254.1997471
Median Absolute Deviation (MAD)35
Skewness-0.9389392648
Sum4824457
Variance4172.5932
MonotonicityNot monotonic
2023-11-05T01:12:37.117411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
263 171
 
0.9%
279 165
 
0.9%
265 163
 
0.9%
274 160
 
0.8%
271 159
 
0.8%
258 158
 
0.8%
266 157
 
0.8%
267 156
 
0.8%
251 154
 
0.8%
277 153
 
0.8%
Other values (341) 17383
91.6%
ValueCountFrequency (%)
50 1
< 0.1%
51 1
< 0.1%
55 1
< 0.1%
58 2
< 0.1%
59 1
< 0.1%
ValueCountFrequency (%)
421 1
< 0.1%
414 1
< 0.1%
412 1
< 0.1%
408 2
< 0.1%
404 1
< 0.1%

Aggression
Real number (ℝ)

Distinct88
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.5912851
Minimum9
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:37.280812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile24
Q144
median58
Q369
95-th percentile80
Maximum96
Range87
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.14041402
Coefficient of variation (CV)0.308329156
Kurtosis-0.5939281574
Mean55.5912851
Median Absolute Deviation (MAD)12
Skewness-0.4257833486
Sum1055067
Variance293.7937927
MonotonicityNot monotonic
2023-11-05T01:12:37.432008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 515
 
2.7%
58 511
 
2.7%
62 503
 
2.7%
68 500
 
2.6%
60 490
 
2.6%
65 490
 
2.6%
55 484
 
2.6%
66 465
 
2.5%
59 457
 
2.4%
64 449
 
2.4%
Other values (78) 14115
74.4%
ValueCountFrequency (%)
9 1
 
< 0.1%
10 1
 
< 0.1%
11 3
 
< 0.1%
12 8
< 0.1%
13 10
0.1%
ValueCountFrequency (%)
96 1
 
< 0.1%
95 2
 
< 0.1%
94 2
 
< 0.1%
93 6
< 0.1%
92 9
< 0.1%

Interceptions
Real number (ℝ)

Distinct89
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.39448865
Minimum3
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:37.637258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile13
Q125
median53
Q364
95-th percentile74
Maximum91
Range88
Interquartile range (IQR)39

Descriptive statistics

Standard deviation20.69807834
Coefficient of variation (CV)0.4461322658
Kurtosis-1.253219483
Mean46.39448865
Median Absolute Deviation (MAD)15
Skewness-0.2951921908
Sum880521
Variance428.410447
MonotonicityNot monotonic
2023-11-05T01:12:37.827080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 521
 
2.7%
60 515
 
2.7%
63 504
 
2.7%
65 496
 
2.6%
58 493
 
2.6%
59 478
 
2.5%
64 471
 
2.5%
66 465
 
2.5%
67 420
 
2.2%
61 416
 
2.2%
Other values (79) 14200
74.8%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 3
 
< 0.1%
5 3
 
< 0.1%
6 60
0.3%
7 76
0.4%
ValueCountFrequency (%)
91 2
 
< 0.1%
90 3
 
< 0.1%
89 2
 
< 0.1%
88 4
 
< 0.1%
87 12
0.1%

Positioning
Real number (ℝ)

Distinct94
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.29896201
Minimum2
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:38.004951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q140
median55
Q364
95-th percentile75
Maximum95
Range93
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.42870113
Coefficient of variation (CV)0.3862644547
Kurtosis-0.2285261369
Mean50.29896201
Median Absolute Deviation (MAD)11
Skewness-0.7863617014
Sum954624
Variance377.4744277
MonotonicityNot monotonic
2023-11-05T01:12:38.165601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 608
 
3.2%
62 558
 
2.9%
59 556
 
2.9%
60 556
 
2.9%
65 553
 
2.9%
64 543
 
2.9%
63 524
 
2.8%
55 518
 
2.7%
57 516
 
2.7%
61 489
 
2.6%
Other values (84) 13558
71.4%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 7
 
< 0.1%
4 111
0.6%
5 154
0.8%
6 190
1.0%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 3
< 0.1%
93 4
< 0.1%
92 4
< 0.1%
91 4
< 0.1%

Vision
Real number (ℝ)

Distinct86
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.85710522
Minimum9
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:38.307636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile30
Q145
median55
Q364
95-th percentile74
Maximum95
Range86
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.70857361
Coefficient of variation (CV)0.2545360274
Kurtosis-0.3685198361
Mean53.85710522
Median Absolute Deviation (MAD)10
Skewness-0.3430614539
Sum1022154
Variance187.9249905
MonotonicityNot monotonic
2023-11-05T01:12:38.450320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 641
 
3.4%
58 631
 
3.3%
55 612
 
3.2%
62 600
 
3.2%
60 593
 
3.1%
65 557
 
2.9%
64 553
 
2.9%
57 524
 
2.8%
52 511
 
2.7%
54 508
 
2.7%
Other values (76) 13249
69.8%
ValueCountFrequency (%)
9 1
 
< 0.1%
10 4
 
< 0.1%
11 12
0.1%
12 10
0.1%
13 12
0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 1
 
< 0.1%
93 1
 
< 0.1%
91 1
 
< 0.1%
90 6
< 0.1%

Penalties
Real number (ℝ)

Distinct87
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.05790611
Minimum6
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:38.595208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile17
Q139
median49
Q360
95-th percentile72
Maximum92
Range86
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.655999
Coefficient of variation (CV)0.3257736399
Kurtosis-0.2585331584
Mean48.05790611
Median Absolute Deviation (MAD)11
Skewness-0.3412844193
Sum912091
Variance245.1103046
MonotonicityNot monotonic
2023-11-05T01:12:38.738003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 526
 
2.8%
45 520
 
2.7%
49 518
 
2.7%
42 507
 
2.7%
40 503
 
2.7%
58 484
 
2.6%
48 484
 
2.6%
60 477
 
2.5%
44 475
 
2.5%
41 455
 
2.4%
Other values (77) 14030
73.9%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 5
 
< 0.1%
8 10
 
0.1%
9 5
 
< 0.1%
10 72
0.4%
ValueCountFrequency (%)
92 5
< 0.1%
91 4
< 0.1%
90 8
< 0.1%
89 2
 
< 0.1%
88 4
< 0.1%

Composure
Real number (ℝ)

Distinct85
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.07366036
Minimum12
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:38.884034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile37
Q150
median59
Q367
95-th percentile76
Maximum96
Range84
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.0791097
Coefficient of variation (CV)0.2079963554
Kurtosis0.2233449485
Mean58.07366036
Median Absolute Deviation (MAD)8
Skewness-0.4409056345
Sum1102180
Variance145.9048911
MonotonicityNot monotonic
2023-11-05T01:12:39.061086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 724
 
3.8%
62 705
 
3.7%
60 703
 
3.7%
65 693
 
3.7%
55 665
 
3.5%
64 648
 
3.4%
59 618
 
3.3%
57 590
 
3.1%
68 589
 
3.1%
63 585
 
3.1%
Other values (75) 12459
65.6%
ValueCountFrequency (%)
12 4
< 0.1%
13 3
< 0.1%
14 2
< 0.1%
15 3
< 0.1%
16 2
< 0.1%
ValueCountFrequency (%)
96 1
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
93 2
< 0.1%
92 3
< 0.1%

Defending
Real number (ℝ)

Distinct247
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.9092681
Minimum20
Maximum272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:39.217991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile39
Q183
median159
Q3191
95-th percentile220
Maximum272
Range252
Interquartile range (IQR)108

Descriptive statistics

Standard deviation61.21947226
Coefficient of variation (CV)0.4375655242
Kurtosis-1.253208635
Mean139.9092681
Median Absolute Deviation (MAD)45
Skewness-0.3209760227
Sum2655338
Variance3747.823783
MonotonicityNot monotonic
2023-11-05T01:12:39.360560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194 221
 
1.2%
184 213
 
1.1%
193 204
 
1.1%
181 202
 
1.1%
188 198
 
1.0%
189 196
 
1.0%
192 195
 
1.0%
187 195
 
1.0%
185 193
 
1.0%
186 192
 
1.0%
Other values (237) 16970
89.4%
ValueCountFrequency (%)
20 1
 
< 0.1%
21 2
< 0.1%
23 1
 
< 0.1%
24 3
< 0.1%
25 4
< 0.1%
ValueCountFrequency (%)
272 2
< 0.1%
267 1
< 0.1%
266 1
< 0.1%
264 2
< 0.1%
263 1
< 0.1%

Marking
Real number (ℝ)

Distinct92
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.55919701
Minimum3
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:39.509202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile12
Q129
median52
Q363
95-th percentile73
Maximum94
Range91
Interquartile range (IQR)34

Descriptive statistics

Standard deviation20.13932429
Coefficient of variation (CV)0.4325530848
Kurtosis-1.0924779
Mean46.55919701
Median Absolute Deviation (MAD)15
Skewness-0.3532880848
Sum883647
Variance405.5923829
MonotonicityNot monotonic
2023-11-05T01:12:39.661353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 560
 
3.0%
65 512
 
2.7%
62 511
 
2.7%
58 500
 
2.6%
64 482
 
2.5%
63 460
 
2.4%
61 453
 
2.4%
55 453
 
2.4%
59 449
 
2.4%
66 447
 
2.4%
Other values (82) 14152
74.6%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 2
 
< 0.1%
5 67
0.4%
6 75
0.4%
7 103
0.5%
ValueCountFrequency (%)
94 1
 
< 0.1%
93 1
 
< 0.1%
92 1
 
< 0.1%
91 1
 
< 0.1%
90 4
< 0.1%

Standing Tackle
Real number (ℝ)

Distinct87
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.705622
Minimum5
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:39.796643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q127
median55
Q365
95-th percentile75
Maximum93
Range88
Interquartile range (IQR)38

Descriptive statistics

Standard deviation21.36768961
Coefficient of variation (CV)0.4479071589
Kurtosis-1.270049875
Mean47.705622
Median Absolute Deviation (MAD)15
Skewness-0.3681281029
Sum905405
Variance456.5781595
MonotonicityNot monotonic
2023-11-05T01:12:39.936166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 637
 
3.4%
65 619
 
3.3%
63 596
 
3.1%
62 564
 
3.0%
66 548
 
2.9%
67 518
 
2.7%
68 497
 
2.6%
70 446
 
2.3%
61 429
 
2.3%
59 427
 
2.2%
Other values (77) 13698
72.2%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 2
 
< 0.1%
7 10
 
0.1%
8 18
 
0.1%
9 45
0.2%
ValueCountFrequency (%)
93 1
 
< 0.1%
90 3
 
< 0.1%
89 4
 
< 0.1%
88 5
< 0.1%
87 11
0.1%

Sliding Tackle
Real number (ℝ)

Distinct85
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.64444913
Minimum4
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:40.084570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile12
Q125
median52
Q363
95-th percentile73
Maximum90
Range86
Interquartile range (IQR)38

Descriptive statistics

Standard deviation20.92208715
Coefficient of variation (CV)0.4583708984
Kurtosis-1.323303822
Mean45.64444913
Median Absolute Deviation (MAD)16
Skewness-0.297981489
Sum866286
Variance437.7337308
MonotonicityNot monotonic
2023-11-05T01:12:40.230916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 625
 
3.3%
64 578
 
3.0%
61 528
 
2.8%
65 526
 
2.8%
63 524
 
2.8%
60 511
 
2.7%
59 462
 
2.4%
58 460
 
2.4%
13 450
 
2.4%
66 433
 
2.3%
Other values (75) 13882
73.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
6 4
 
< 0.1%
7 4
 
< 0.1%
8 20
0.1%
9 41
0.2%
ValueCountFrequency (%)
90 3
 
< 0.1%
88 2
 
< 0.1%
87 5
 
< 0.1%
86 9
< 0.1%
85 13
0.1%

Goalkeeping
Real number (ℝ)

Distinct259
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.43669319
Minimum10
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:40.374329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile41
Q148
median53
Q359
95-th percentile322
Maximum440
Range430
Interquartile range (IQR)11

Descriptive statistics

Standard deviation84.66557941
Coefficient of variation (CV)1.039649034
Kurtosis4.846421277
Mean81.43669319
Median Absolute Deviation (MAD)6
Skewness2.561242795
Sum1545587
Variance7168.260338
MonotonicityNot monotonic
2023-11-05T01:12:40.520638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 977
 
5.1%
51 939
 
4.9%
53 937
 
4.9%
50 912
 
4.8%
54 880
 
4.6%
49 846
 
4.5%
55 836
 
4.4%
56 817
 
4.3%
48 752
 
4.0%
47 740
 
3.9%
Other values (249) 10343
54.5%
ValueCountFrequency (%)
10 2
 
< 0.1%
13 2
 
< 0.1%
15 9
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
440 1
< 0.1%
439 2
< 0.1%
437 1
< 0.1%
435 1
< 0.1%
424 1
< 0.1%

GK Diving
Real number (ℝ)

Distinct69
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.41630223
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:40.662283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median11
Q314
95-th percentile66
Maximum90
Range88
Interquartile range (IQR)6

Descriptive statistics

Standard deviation17.55419442
Coefficient of variation (CV)1.069314768
Kurtosis4.591077911
Mean16.41630223
Median Absolute Deviation (MAD)3
Skewness2.471044397
Sum311565
Variance308.1497416
MonotonicityNot monotonic
2023-11-05T01:12:40.804806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 1684
8.9%
7 1672
8.8%
11 1662
8.8%
13 1659
8.7%
12 1654
8.7%
9 1651
8.7%
14 1630
8.6%
10 1625
8.6%
6 1461
7.7%
15 1213
 
6.4%
Other values (59) 3068
16.2%
ValueCountFrequency (%)
2 5
 
< 0.1%
3 14
 
0.1%
4 14
 
0.1%
5 494
 
2.6%
6 1461
7.7%
ValueCountFrequency (%)
90 1
 
< 0.1%
89 2
< 0.1%
88 3
< 0.1%
87 2
< 0.1%
86 4
< 0.1%

GK Handling
Real number (ℝ)

Distinct70
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.20712366
Minimum2
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:40.948870image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median11
Q314
95-th percentile63
Maximum92
Range90
Interquartile range (IQR)6

Descriptive statistics

Standard deviation16.81630496
Coefficient of variation (CV)1.037587255
Kurtosis4.566949351
Mean16.20712366
Median Absolute Deviation (MAD)3
Skewness2.46189166
Sum307595
Variance282.7881124
MonotonicityNot monotonic
2023-11-05T01:12:41.091508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1743
9.2%
12 1699
9.0%
11 1683
8.9%
7 1680
8.9%
13 1673
8.8%
9 1651
8.7%
14 1626
8.6%
8 1616
8.5%
6 1345
7.1%
15 1164
 
6.1%
Other values (60) 3099
16.3%
ValueCountFrequency (%)
2 7
 
< 0.1%
3 17
 
0.1%
4 11
 
0.1%
5 474
 
2.5%
6 1345
7.1%
ValueCountFrequency (%)
92 1
< 0.1%
89 1
< 0.1%
88 1
< 0.1%
87 1
< 0.1%
86 1
< 0.1%

GK Kicking
Real number (ℝ)

Distinct79
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.07645292
Minimum2
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:41.231364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median11
Q314
95-th percentile62
Maximum93
Range91
Interquartile range (IQR)6

Descriptive statistics

Standard deviation16.49110304
Coefficient of variation (CV)1.025792388
Kurtosis4.614980882
Mean16.07645292
Median Absolute Deviation (MAD)3
Skewness2.462723519
Sum305115
Variance271.9564794
MonotonicityNot monotonic
2023-11-05T01:12:41.372428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 1712
9.0%
9 1706
9.0%
7 1689
8.9%
13 1685
8.9%
14 1681
8.9%
8 1617
8.5%
10 1616
8.5%
11 1587
8.4%
6 1400
7.4%
15 1150
 
6.1%
Other values (69) 3136
16.5%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 16
 
0.1%
4 12
 
0.1%
5 502
 
2.6%
6 1400
7.4%
ValueCountFrequency (%)
93 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
88 1
< 0.1%
87 2
< 0.1%

GK Positioning
Real number (ℝ)

Distinct76
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.21718742
Minimum2
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:41.513158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median11
Q314
95-th percentile64
Maximum91
Range89
Interquartile range (IQR)6

Descriptive statistics

Standard deviation17.00223891
Coefficient of variation (CV)1.048408609
Kurtosis4.763669676
Mean16.21718742
Median Absolute Deviation (MAD)3
Skewness2.49171618
Sum307786
Variance289.0761278
MonotonicityNot monotonic
2023-11-05T01:12:41.654255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 1756
9.3%
7 1701
9.0%
10 1692
8.9%
11 1692
8.9%
9 1658
8.7%
12 1643
8.7%
14 1614
8.5%
13 1557
8.2%
6 1313
6.9%
15 1240
 
6.5%
Other values (66) 3113
16.4%
ValueCountFrequency (%)
2 7
 
< 0.1%
3 14
 
0.1%
4 18
 
0.1%
5 502
 
2.6%
6 1313
6.9%
ValueCountFrequency (%)
91 2
< 0.1%
90 1
< 0.1%
89 1
< 0.1%
88 1
< 0.1%
87 2
< 0.1%

GK Reflexes
Real number (ℝ)

Distinct70
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.51962696
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:42.404279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median11
Q314
95-th percentile67
Maximum90
Range88
Interquartile range (IQR)6

Descriptive statistics

Standard deviation17.85407861
Coefficient of variation (CV)1.080779769
Kurtosis4.664639557
Mean16.51962696
Median Absolute Deviation (MAD)3
Skewness2.484474749
Sum313526
Variance318.768123
MonotonicityNot monotonic
2023-11-05T01:12:42.547535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 1704
9.0%
10 1694
8.9%
11 1682
8.9%
7 1677
8.8%
13 1662
8.8%
8 1643
8.7%
12 1642
8.7%
14 1638
8.6%
6 1401
7.4%
15 1148
 
6.0%
Other values (60) 3088
16.3%
ValueCountFrequency (%)
2 3
 
< 0.1%
3 18
 
0.1%
4 18
 
0.1%
5 495
 
2.6%
6 1401
7.4%
ValueCountFrequency (%)
90 4
< 0.1%
89 6
< 0.1%
88 7
< 0.1%
87 3
 
< 0.1%
86 9
< 0.1%

Total Stats
Real number (ℝ)

Distinct1420
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1595.286949
Minimum747
Maximum2316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:42.686127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum747
5-th percentile1047
Q11452
median1627
Q31781
95-th percentile1980
Maximum2316
Range1569
Interquartile range (IQR)329

Descriptive statistics

Standard deviation269.8747889
Coefficient of variation (CV)0.16917006
Kurtosis0.337619737
Mean1595.286949
Median Absolute Deviation (MAD)163
Skewness-0.6466736361
Sum30276951
Variance72832.40169
MonotonicityNot monotonic
2023-11-05T01:12:42.838270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1681 45
 
0.2%
1532 44
 
0.2%
1666 43
 
0.2%
1711 42
 
0.2%
1672 40
 
0.2%
1528 40
 
0.2%
1686 40
 
0.2%
1673 40
 
0.2%
1776 40
 
0.2%
1607 40
 
0.2%
Other values (1410) 18565
97.8%
ValueCountFrequency (%)
747 1
< 0.1%
749 1
< 0.1%
757 1
< 0.1%
760 1
< 0.1%
763 1
< 0.1%
ValueCountFrequency (%)
2316 1
< 0.1%
2304 1
< 0.1%
2303 1
< 0.1%
2288 1
< 0.1%
2282 1
< 0.1%

Base Stats
Real number (ℝ)

Distinct250
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean355.7021972
Minimum232
Maximum498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:43.007277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum232
5-th percentile288
Q1327
median356
Q3384
95-th percentile423
Maximum498
Range266
Interquartile range (IQR)57

Descriptive statistics

Standard deviation40.76111739
Coefficient of variation (CV)0.114593381
Kurtosis-0.3064302161
Mean355.7021972
Median Absolute Deviation (MAD)29
Skewness0.03554711043
Sum6750872
Variance1661.468691
MonotonicityNot monotonic
2023-11-05T01:12:43.160257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
358 209
 
1.1%
365 194
 
1.0%
353 193
 
1.0%
363 192
 
1.0%
345 191
 
1.0%
361 189
 
1.0%
368 187
 
1.0%
371 187
 
1.0%
357 185
 
1.0%
349 184
 
1.0%
Other values (240) 17068
89.9%
ValueCountFrequency (%)
232 1
< 0.1%
233 1
< 0.1%
238 2
< 0.1%
239 2
< 0.1%
240 2
< 0.1%
ValueCountFrequency (%)
498 1
< 0.1%
497 1
< 0.1%
490 1
< 0.1%
489 1
< 0.1%
485 1
< 0.1%

W/F
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.940513199
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:43.280499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q33
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6684494842
Coefficient of variation (CV)0.227324089
Kurtosis0.6211832605
Mean2.940513199
Median Absolute Deviation (MAD)0
Skewness0.222012511
Sum55808
Variance0.4468247129
MonotonicityNot monotonic
2023-11-05T01:12:43.396568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
3 11695
61.6%
2 4141
 
21.8%
4 2722
 
14.3%
5 283
 
1.5%
1 138
 
0.7%
ValueCountFrequency (%)
1 138
 
0.7%
2 4141
 
21.8%
3 11695
61.6%
4 2722
 
14.3%
5 283
 
1.5%
ValueCountFrequency (%)
5 283
 
1.5%
4 2722
 
14.3%
3 11695
61.6%
2 4141
 
21.8%
1 138
 
0.7%

SM
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.364982349
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:43.515263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7661139032
Coefficient of variation (CV)0.3239406432
Kurtosis-0.0685505422
Mean2.364982349
Median Absolute Deviation (MAD)1
Skewness0.2235971186
Sum44885
Variance0.5869305127
MonotonicityNot monotonic
2023-11-05T01:12:43.626698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2 9142
48.2%
3 6577
34.7%
1 2075
 
10.9%
4 1130
 
6.0%
5 55
 
0.3%
ValueCountFrequency (%)
1 2075
 
10.9%
2 9142
48.2%
3 6577
34.7%
4 1130
 
6.0%
5 55
 
0.3%
ValueCountFrequency (%)
5 55
 
0.3%
4 1130
 
6.0%
3 6577
34.7%
2 9142
48.2%
1 2075
 
10.9%

A/W
Real number (ℝ)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.226460825
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:43.740898image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5286935609
Coefficient of variation (CV)0.2374591796
Kurtosis-0.1894466432
Mean2.226460825
Median Absolute Deviation (MAD)0
Skewness0.1689144755
Sum42256
Variance0.2795168813
MonotonicityNot monotonic
2023-11-05T01:12:43.858768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2 12701
66.9%
3 5288
27.9%
1 990
 
5.2%
ValueCountFrequency (%)
1 990
 
5.2%
2 12701
66.9%
3 5288
27.9%
ValueCountFrequency (%)
3 5288
27.9%
2 12701
66.9%
1 990
 
5.2%

D/W
Real number (ℝ)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.082775699
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:43.995978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5077624536
Coefficient of variation (CV)0.2437912319
Kurtosis0.7316643564
Mean2.082775699
Median Absolute Deviation (MAD)0
Skewness0.1389507676
Sum39529
Variance0.2578227093
MonotonicityNot monotonic
2023-11-05T01:12:44.173455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2 13956
73.5%
3 3297
 
17.4%
1 1726
 
9.1%
ValueCountFrequency (%)
1 1726
 
9.1%
2 13956
73.5%
3 3297
 
17.4%
ValueCountFrequency (%)
3 3297
 
17.4%
2 13956
73.5%
1 1726
 
9.1%

IR
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.091627588
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:44.360078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum5
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3617602269
Coefficient of variation (CV)0.3313952769
Kurtosis24.82371609
Mean1.091627588
Median Absolute Deviation (MAD)0
Skewness4.639035968
Sum20718
Variance0.1308704618
MonotonicityNot monotonic
2023-11-05T01:12:44.528986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
1 17629
92.9%
2 1018
 
5.4%
3 281
 
1.5%
4 45
 
0.2%
5 6
 
< 0.1%
ValueCountFrequency (%)
1 17629
92.9%
2 1018
 
5.4%
3 281
 
1.5%
4 45
 
0.2%
5 6
 
< 0.1%
ValueCountFrequency (%)
5 6
 
< 0.1%
4 45
 
0.2%
3 281
 
1.5%
2 1018
 
5.4%
1 17629
92.9%

PAC
Real number (ℝ)

Distinct70
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.45397545
Minimum25
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:44.761527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile49
Q161
median68
Q375
95-th percentile84
Maximum96
Range71
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.67785932
Coefficient of variation (CV)0.1582984435
Kurtosis0.6741991404
Mean67.45397545
Median Absolute Deviation (MAD)7
Skewness-0.4544097453
Sum1280209
Variance114.0166797
MonotonicityNot monotonic
2023-11-05T01:12:44.981808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 834
 
4.4%
68 806
 
4.2%
67 803
 
4.2%
65 781
 
4.1%
71 765
 
4.0%
69 762
 
4.0%
64 734
 
3.9%
70 708
 
3.7%
73 682
 
3.6%
72 663
 
3.5%
Other values (60) 11441
60.3%
ValueCountFrequency (%)
25 1
 
< 0.1%
28 2
 
< 0.1%
29 2
 
< 0.1%
30 22
0.1%
31 20
0.1%
ValueCountFrequency (%)
96 3
 
< 0.1%
95 3
 
< 0.1%
94 20
0.1%
93 39
0.2%
92 47
0.2%

SHO
Real number (ℝ)

Distinct78
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.45703146
Minimum16
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:45.199608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile28
Q144
median56
Q364
95-th percentile73
Maximum93
Range77
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.82742519
Coefficient of variation (CV)0.2586642919
Kurtosis-0.6495956139
Mean53.45703146
Median Absolute Deviation (MAD)9
Skewness-0.3773815127
Sum1014561
Variance191.1976873
MonotonicityNot monotonic
2023-11-05T01:12:45.470934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 630
 
3.3%
58 629
 
3.3%
62 611
 
3.2%
61 607
 
3.2%
63 599
 
3.2%
59 586
 
3.1%
64 568
 
3.0%
65 566
 
3.0%
57 554
 
2.9%
56 526
 
2.8%
Other values (68) 13103
69.0%
ValueCountFrequency (%)
16 1
 
< 0.1%
17 2
 
< 0.1%
18 3
 
< 0.1%
19 3
 
< 0.1%
20 9
< 0.1%
ValueCountFrequency (%)
93 1
< 0.1%
92 2
< 0.1%
91 2
< 0.1%
90 2
< 0.1%
89 1
< 0.1%

PAS
Real number (ℝ)

Distinct68
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.68101586
Minimum25
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:45.679684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile40
Q151
median58
Q364
95-th percentile73
Maximum93
Range68
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.08185717
Coefficient of variation (CV)0.174786401
Kurtosis-0.0296601063
Mean57.68101586
Median Absolute Deviation (MAD)7
Skewness-0.2164597558
Sum1094728
Variance101.6438439
MonotonicityNot monotonic
2023-11-05T01:12:45.845826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 843
 
4.4%
62 801
 
4.2%
61 797
 
4.2%
58 782
 
4.1%
59 779
 
4.1%
57 764
 
4.0%
63 715
 
3.8%
56 704
 
3.7%
55 686
 
3.6%
54 658
 
3.5%
Other values (58) 11450
60.3%
ValueCountFrequency (%)
25 5
 
< 0.1%
26 7
 
< 0.1%
27 12
0.1%
28 12
0.1%
29 27
0.1%
ValueCountFrequency (%)
93 2
< 0.1%
91 3
< 0.1%
90 2
< 0.1%
89 1
 
< 0.1%
88 4
< 0.1%

DRI
Real number (ℝ)

Distinct69
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.87501976
Minimum25
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:45.992085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile44
Q157
median64
Q369
95-th percentile78
Maximum95
Range70
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.927414794
Coefficient of variation (CV)0.1578912393
Kurtosis0.4580539975
Mean62.87501976
Median Absolute Deviation (MAD)6
Skewness-0.5121806462
Sum1193305
Variance98.55356449
MonotonicityNot monotonic
2023-11-05T01:12:46.181102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 917
 
4.8%
63 896
 
4.7%
66 890
 
4.7%
65 881
 
4.6%
67 833
 
4.4%
68 829
 
4.4%
62 814
 
4.3%
69 769
 
4.1%
61 700
 
3.7%
60 690
 
3.6%
Other values (59) 10760
56.7%
ValueCountFrequency (%)
25 1
 
< 0.1%
27 3
 
< 0.1%
28 1
 
< 0.1%
29 7
 
< 0.1%
30 18
0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 1
 
< 0.1%
92 3
 
< 0.1%
91 4
 
< 0.1%
90 12
0.1%

DEF
Real number (ℝ)

Distinct78
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.86622056
Minimum12
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:46.347225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile23
Q135
median53
Q363
95-th percentile73
Maximum91
Range79
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.44321277
Coefficient of variation (CV)0.3297465215
Kurtosis-1.1197498
Mean49.86622056
Median Absolute Deviation (MAD)13
Skewness-0.2220157905
Sum946411
Variance270.3792462
MonotonicityNot monotonic
2023-11-05T01:12:46.505766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 612
 
3.2%
62 589
 
3.1%
61 568
 
3.0%
60 552
 
2.9%
64 519
 
2.7%
65 502
 
2.6%
66 498
 
2.6%
59 486
 
2.6%
58 454
 
2.4%
57 423
 
2.2%
Other values (68) 13776
72.6%
ValueCountFrequency (%)
12 1
 
< 0.1%
15 8
 
< 0.1%
16 28
 
0.1%
17 46
0.2%
18 109
0.6%
ValueCountFrequency (%)
91 1
 
< 0.1%
90 1
 
< 0.1%
89 2
 
< 0.1%
88 2
 
< 0.1%
87 8
< 0.1%

PHY
Real number (ℝ)

Distinct63
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.36893409
Minimum28
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.4 KiB
2023-11-05T01:12:46.711901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile47
Q158
median65
Q371
95-th percentile79
Maximum91
Range63
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.601883146
Coefficient of variation (CV)0.1491695223
Kurtosis-0.1416933351
Mean64.36893409
Median Absolute Deviation (MAD)7
Skewness-0.4102686126
Sum1221658
Variance92.19615995
MonotonicityNot monotonic
2023-11-05T01:12:46.868084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 850
 
4.5%
67 829
 
4.4%
70 787
 
4.1%
69 786
 
4.1%
68 774
 
4.1%
65 746
 
3.9%
71 735
 
3.9%
63 714
 
3.8%
62 699
 
3.7%
64 678
 
3.6%
Other values (53) 11381
60.0%
ValueCountFrequency (%)
28 2
 
< 0.1%
29 2
 
< 0.1%
31 3
< 0.1%
32 1
 
< 0.1%
33 6
< 0.1%
ValueCountFrequency (%)
91 4
 
< 0.1%
90 2
 
< 0.1%
89 4
 
< 0.1%
88 4
 
< 0.1%
87 14
0.1%